Overview

Brought to you by YData

Dataset statistics

Number of variables62
Number of observations438
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.3 KiB
Average record size in memory496.3 B

Variable types

Numeric12
Categorical50

Alerts

acousticness is highly overall correlated with energyHigh correlation
backing_instruments is highly overall correlated with style_RockHigh correlation
country_Andorra is highly overall correlated with loudness(dB)High correlation
danceability is highly overall correlated with happinessHigh correlation
energy is highly overall correlated with acousticness and 2 other fieldsHigh correlation
gender_Mix is highly overall correlated with main_singersHigh correlation
happiness is highly overall correlated with danceability and 1 other fieldsHigh correlation
loudness(dB) is highly overall correlated with country_Andorra and 1 other fieldsHigh correlation
main_singers is highly overall correlated with gender_MixHigh correlation
style_Rock is highly overall correlated with backing_instrumentsHigh correlation
country_Andorra is highly imbalanced (97.7%) Imbalance
country_Armenia is highly imbalanced (84.3%) Imbalance
country_Australia is highly imbalanced (89.6%) Imbalance
country_Austria is highly imbalanced (84.3%) Imbalance
country_Azerbaijan is highly imbalanced (81.9%) Imbalance
country_Belarus is highly imbalanced (83.1%) Imbalance
country_Belgium is highly imbalanced (80.7%) Imbalance
country_Bosnia and Herzegovina is highly imbalanced (91.0%) Imbalance
country_Bulgaria is highly imbalanced (84.3%) Imbalance
country_Croatia is highly imbalanced (83.1%) Imbalance
country_Cyprus is highly imbalanced (81.9%) Imbalance
country_Czech Republic is highly imbalanced (86.8%) Imbalance
country_Denmark is highly imbalanced (81.9%) Imbalance
country_Estonia is highly imbalanced (80.7%) Imbalance
country_Finland is highly imbalanced (80.7%) Imbalance
country_Georgia is highly imbalanced (84.3%) Imbalance
country_Greece is highly imbalanced (80.7%) Imbalance
country_Hungary is highly imbalanced (84.3%) Imbalance
country_Iceland is highly imbalanced (80.7%) Imbalance
country_Ireland is highly imbalanced (80.7%) Imbalance
country_Israel is highly imbalanced (83.1%) Imbalance
country_Latvia is highly imbalanced (81.9%) Imbalance
country_Lithuania is highly imbalanced (80.7%) Imbalance
country_Malta is highly imbalanced (80.7%) Imbalance
country_Moldova is highly imbalanced (81.9%) Imbalance
country_Montenegro is highly imbalanced (84.3%) Imbalance
country_Netherlands is highly imbalanced (81.9%) Imbalance
country_North Macedonia is highly imbalanced (81.9%) Imbalance
country_Norway is highly imbalanced (81.9%) Imbalance
country_Poland is highly imbalanced (83.1%) Imbalance
country_Portugal is highly imbalanced (85.5%) Imbalance
country_Romania is highly imbalanced (81.9%) Imbalance
country_Russia is highly imbalanced (84.3%) Imbalance
country_San Marino is highly imbalanced (84.3%) Imbalance
country_Serbia is highly imbalanced (83.1%) Imbalance
country_Slovakia is highly imbalanced (94.1%) Imbalance
country_Slovenia is highly imbalanced (81.9%) Imbalance
country_Sweden is highly imbalanced (83.1%) Imbalance
country_Switzerland is highly imbalanced (80.7%) Imbalance
country_Turkey is highly imbalanced (94.1%) Imbalance
country_Ukraine is highly imbalanced (84.3%) Imbalance
style_Opera is highly imbalanced (94.1%) Imbalance
style_Rock is highly imbalanced (63.1%) Imbalance
style_Traditional is highly imbalanced (59.8%) Imbalance
gender_Mix is highly imbalanced (57.4%) Imbalance
acousticness has 61 (13.9%) zeros Zeros
backing_dancers has 290 (66.2%) zeros Zeros
backing_singers has 277 (63.2%) zeros Zeros
backing_instruments has 297 (67.8%) zeros Zeros

Reproduction

Analysis started2024-10-29 12:06:16.223866
Analysis finished2024-10-29 12:06:35.507159
Duration19.28 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

semi_draw_position
Real number (ℝ)

Distinct19
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2945205
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:35.558990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q314
95-th percentile17
Maximum19
Range18
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.0970011
Coefficient of variation (CV)0.54838774
Kurtosis-1.1559081
Mean9.2945205
Median Absolute Deviation (MAD)4
Skewness0.028518578
Sum4071
Variance25.979421
MonotonicityNot monotonic
2024-10-29T13:06:35.653835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 26
 
5.9%
8 26
 
5.9%
10 26
 
5.9%
4 25
 
5.7%
5 25
 
5.7%
6 25
 
5.7%
7 25
 
5.7%
9 25
 
5.7%
12 25
 
5.7%
13 25
 
5.7%
Other values (9) 185
42.2%
ValueCountFrequency (%)
1 26
5.9%
2 24
5.5%
3 24
5.5%
4 25
5.7%
5 25
5.7%
6 25
5.7%
7 25
5.7%
8 26
5.9%
9 25
5.7%
10 26
5.9%
ValueCountFrequency (%)
19 4
 
0.9%
18 14
3.2%
17 20
4.6%
16 25
5.7%
15 25
5.7%
14 25
5.7%
13 25
5.7%
12 25
5.7%
11 24
5.5%
10 26
5.9%

language
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
1
315 
0
123 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters438
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 315
71.9%
0 123
 
28.1%

Length

2024-10-29T13:06:35.745088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:35.820903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 315
71.9%
0 123
 
28.1%

Most occurring characters

ValueCountFrequency (%)
1 315
71.9%
0 123
 
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 315
71.9%
0 123
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 315
71.9%
0 123
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 315
71.9%
0 123
 
28.1%

main_singers
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3105023
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:35.892204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.87132253
Coefficient of variation (CV)0.66487677
Kurtosis14.587233
Mean1.3105023
Median Absolute Deviation (MAD)0
Skewness3.6691683
Sum574
Variance0.75920295
MonotonicityNot monotonic
2024-10-29T13:06:35.975005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 364
83.1%
2 45
 
10.3%
3 13
 
3.0%
6 7
 
1.6%
4 6
 
1.4%
5 3
 
0.7%
ValueCountFrequency (%)
1 364
83.1%
2 45
 
10.3%
3 13
 
3.0%
4 6
 
1.4%
5 3
 
0.7%
6 7
 
1.6%
ValueCountFrequency (%)
6 7
 
1.6%
5 3
 
0.7%
4 6
 
1.4%
3 13
 
3.0%
2 45
 
10.3%
1 364
83.1%

key
Real number (ℝ)

Distinct24
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.36381
Minimum130.81
Maximum739.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:36.055545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum130.81
5-th percentile146.83
Q1196
median261.63
Q3386.4975
95-th percentile554.37
Maximum739.99
Range609.18
Interquartile range (IQR)190.4975

Descriptive statistics

Standard deviation142.13033
Coefficient of variation (CV)0.47006396
Kurtosis1.7040217
Mean302.36381
Median Absolute Deviation (MAD)87.02
Skewness1.342429
Sum132435.35
Variance20201.031
MonotonicityNot monotonic
2024-10-29T13:06:36.141267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
261.63 37
 
8.4%
246.94 28
 
6.4%
164.81 25
 
5.7%
220 25
 
5.7%
196 23
 
5.3%
146.83 23
 
5.3%
174.61 22
 
5.0%
392 22
 
5.0%
233.08 20
 
4.6%
293.66 20
 
4.6%
Other values (14) 193
44.1%
ValueCountFrequency (%)
130.81 18
4.1%
146.83 23
5.3%
164.81 25
5.7%
174.61 22
5.0%
196 23
5.3%
207.65 17
3.9%
220 25
5.7%
233.08 20
4.6%
246.94 28
6.4%
261.63 37
8.4%
ValueCountFrequency (%)
739.99 18
4.1%
622.25 3
 
0.7%
554.37 16
3.7%
493.88 5
 
1.1%
466.16 9
2.1%
440 19
4.3%
415.3 18
4.1%
392 22
5.0%
369.99 15
3.4%
349.23 15
3.4%

BPM
Real number (ℝ)

Distinct98
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.19863
Minimum53
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:36.243628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile77
Q197
median120
Q3130
95-th percentile158
Maximum187
Range134
Interquartile range (IQR)33

Descriptive statistics

Standard deviation24.308925
Coefficient of variation (CV)0.20920148
Kurtosis-0.2690697
Mean116.19863
Median Absolute Deviation (MAD)16
Skewness0.13234719
Sum50895
Variance590.92384
MonotonicityNot monotonic
2024-10-29T13:06:36.355801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128 23
 
5.3%
120 21
 
4.8%
130 20
 
4.6%
125 11
 
2.5%
132 11
 
2.5%
122 11
 
2.5%
138 10
 
2.3%
90 10
 
2.3%
126 9
 
2.1%
123 9
 
2.1%
Other values (88) 303
69.2%
ValueCountFrequency (%)
53 1
 
0.2%
66 1
 
0.2%
67 1
 
0.2%
69 1
 
0.2%
70 2
 
0.5%
72 3
0.7%
73 1
 
0.2%
74 1
 
0.2%
75 6
1.4%
76 4
0.9%
ValueCountFrequency (%)
187 1
 
0.2%
183 1
 
0.2%
180 1
 
0.2%
176 2
0.5%
174 1
 
0.2%
172 3
0.7%
170 4
0.9%
168 1
 
0.2%
164 2
0.5%
163 1
 
0.2%

energy
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.358447
Minimum9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:36.462549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile32.85
Q156
median71
Q383
95-th percentile93
Maximum100
Range91
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.927945
Coefficient of variation (CV)0.27689255
Kurtosis-0.054536417
Mean68.358447
Median Absolute Deviation (MAD)13
Skewness-0.70309856
Sum29941
Variance358.2671
MonotonicityNot monotonic
2024-10-29T13:06:36.565815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 16
 
3.7%
91 14
 
3.2%
70 12
 
2.7%
92 12
 
2.7%
67 12
 
2.7%
87 12
 
2.7%
82 12
 
2.7%
80 11
 
2.5%
64 10
 
2.3%
63 10
 
2.3%
Other values (68) 317
72.4%
ValueCountFrequency (%)
9 1
 
0.2%
10 1
 
0.2%
15 1
 
0.2%
18 2
0.5%
19 1
 
0.2%
20 3
0.7%
21 2
0.5%
26 1
 
0.2%
28 1
 
0.2%
29 2
0.5%
ValueCountFrequency (%)
100 1
 
0.2%
97 2
 
0.5%
96 6
1.4%
95 2
 
0.5%
94 9
2.1%
93 5
 
1.1%
92 12
2.7%
91 14
3.2%
90 6
1.4%
89 8
1.8%

danceability
Real number (ℝ)

High correlation 

Distinct69
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.780822
Minimum17
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:36.664370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile30.85
Q146
median57
Q366
95-th percentile79.15
Maximum92
Range75
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.708064
Coefficient of variation (CV)0.26367599
Kurtosis-0.41232792
Mean55.780822
Median Absolute Deviation (MAD)10
Skewness-0.20668602
Sum24432
Variance216.32714
MonotonicityNot monotonic
2024-10-29T13:06:36.791161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 17
 
3.9%
52 16
 
3.7%
58 15
 
3.4%
63 14
 
3.2%
50 13
 
3.0%
56 13
 
3.0%
57 13
 
3.0%
53 12
 
2.7%
71 11
 
2.5%
61 11
 
2.5%
Other values (59) 303
69.2%
ValueCountFrequency (%)
17 2
0.5%
19 1
 
0.2%
20 1
 
0.2%
21 1
 
0.2%
22 1
 
0.2%
23 1
 
0.2%
25 1
 
0.2%
26 1
 
0.2%
27 4
0.9%
28 4
0.9%
ValueCountFrequency (%)
92 1
 
0.2%
89 1
 
0.2%
88 1
 
0.2%
87 2
 
0.5%
83 6
1.4%
82 2
 
0.5%
81 5
1.1%
80 4
0.9%
79 2
 
0.5%
78 2
 
0.5%

happiness
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.833333
Minimum4
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:36.934425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q128
median41
Q361
95-th percentile86
Maximum97
Range93
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.173068
Coefficient of variation (CV)0.49456656
Kurtosis-0.68268767
Mean44.833333
Median Absolute Deviation (MAD)16
Skewness0.4578008
Sum19637
Variance491.64493
MonotonicityNot monotonic
2024-10-29T13:06:37.055068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 11
 
2.5%
19 10
 
2.3%
32 10
 
2.3%
48 10
 
2.3%
31 9
 
2.1%
39 9
 
2.1%
36 9
 
2.1%
21 9
 
2.1%
28 9
 
2.1%
54 8
 
1.8%
Other values (78) 344
78.5%
ValueCountFrequency (%)
4 1
 
0.2%
7 1
 
0.2%
8 2
 
0.5%
9 3
0.7%
10 3
0.7%
11 2
 
0.5%
12 5
1.1%
13 2
 
0.5%
14 4
0.9%
15 4
0.9%
ValueCountFrequency (%)
97 2
0.5%
96 4
0.9%
93 2
0.5%
92 4
0.9%
89 4
0.9%
88 1
 
0.2%
87 4
0.9%
86 2
0.5%
85 3
0.7%
84 3
0.7%

loudness(dB)
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0981735
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:37.147634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14.25
median6
Q37
95-th percentile10
Maximum18
Range16
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation2.2654395
Coefficient of variation (CV)0.37149477
Kurtosis2.5644806
Mean6.0981735
Median Absolute Deviation (MAD)1
Skewness1.1792052
Sum2671
Variance5.1322163
MonotonicityNot monotonic
2024-10-29T13:06:37.232350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 92
21.0%
6 81
18.5%
4 79
18.0%
7 56
12.8%
8 42
9.6%
3 25
 
5.7%
10 20
 
4.6%
9 19
 
4.3%
11 8
 
1.8%
2 6
 
1.4%
Other values (5) 10
 
2.3%
ValueCountFrequency (%)
2 6
 
1.4%
3 25
 
5.7%
4 79
18.0%
5 92
21.0%
6 81
18.5%
7 56
12.8%
8 42
9.6%
9 19
 
4.3%
10 20
 
4.6%
11 8
 
1.8%
ValueCountFrequency (%)
18 1
 
0.2%
16 1
 
0.2%
15 1
 
0.2%
13 2
 
0.5%
12 5
 
1.1%
11 8
 
1.8%
10 20
 
4.6%
9 19
 
4.3%
8 42
9.6%
7 56
12.8%

acousticness
Real number (ℝ)

High correlation  Zeros 

Distinct80
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.002283
Minimum0
Maximum89
Zeros61
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:37.334373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q333.75
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation24.311997
Coefficient of variation (CV)1.1575883
Kurtosis0.29601661
Mean21.002283
Median Absolute Deviation (MAD)10
Skewness1.1962896
Sum9199
Variance591.07322
MonotonicityNot monotonic
2024-10-29T13:06:37.434558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
 
13.9%
1 48
 
11.0%
11 23
 
5.3%
2 22
 
5.0%
4 18
 
4.1%
5 13
 
3.0%
3 10
 
2.3%
14 10
 
2.3%
6 10
 
2.3%
8 8
 
1.8%
Other values (70) 215
49.1%
ValueCountFrequency (%)
0 61
13.9%
1 48
11.0%
2 22
 
5.0%
3 10
 
2.3%
4 18
 
4.1%
5 13
 
3.0%
6 10
 
2.3%
7 7
 
1.6%
8 8
 
1.8%
9 6
 
1.4%
ValueCountFrequency (%)
89 1
 
0.2%
87 3
0.7%
86 2
0.5%
85 2
0.5%
84 1
 
0.2%
83 3
0.7%
82 2
0.5%
81 1
 
0.2%
80 2
0.5%
79 1
 
0.2%

backing_dancers
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89954338
Minimum0
Maximum5
Zeros290
Zeros (%)66.2%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:37.517530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4631866
Coefficient of variation (CV)1.6265882
Kurtosis0.55464367
Mean0.89954338
Median Absolute Deviation (MAD)0
Skewness1.4034135
Sum394
Variance2.1409151
MonotonicityNot monotonic
2024-10-29T13:06:37.599878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 290
66.2%
4 41
 
9.4%
2 37
 
8.4%
1 36
 
8.2%
3 25
 
5.7%
5 9
 
2.1%
ValueCountFrequency (%)
0 290
66.2%
1 36
 
8.2%
2 37
 
8.4%
3 25
 
5.7%
4 41
 
9.4%
5 9
 
2.1%
ValueCountFrequency (%)
5 9
 
2.1%
4 41
 
9.4%
3 25
 
5.7%
2 37
 
8.4%
1 36
 
8.2%
0 290
66.2%

backing_singers
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1004566
Minimum0
Maximum5
Zeros277
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:37.677660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6105913
Coefficient of variation (CV)1.4635664
Kurtosis-0.26382141
Mean1.1004566
Median Absolute Deviation (MAD)0
Skewness1.0971345
Sum482
Variance2.5940044
MonotonicityNot monotonic
2024-10-29T13:06:37.756561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 277
63.2%
3 48
 
11.0%
2 41
 
9.4%
4 36
 
8.2%
5 19
 
4.3%
1 17
 
3.9%
ValueCountFrequency (%)
0 277
63.2%
1 17
 
3.9%
2 41
 
9.4%
3 48
 
11.0%
4 36
 
8.2%
5 19
 
4.3%
ValueCountFrequency (%)
5 19
 
4.3%
4 36
 
8.2%
3 48
 
11.0%
2 41
 
9.4%
1 17
 
3.9%
0 277
63.2%

backing_instruments
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8196347
Minimum0
Maximum5
Zeros297
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-10-29T13:06:37.832786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.432502
Coefficient of variation (CV)1.7477323
Kurtosis1.5748897
Mean0.8196347
Median Absolute Deviation (MAD)0
Skewness1.6757211
Sum359
Variance2.0520621
MonotonicityNot monotonic
2024-10-29T13:06:37.909971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 297
67.8%
1 47
 
10.7%
2 27
 
6.2%
3 27
 
6.2%
4 23
 
5.3%
5 17
 
3.9%
ValueCountFrequency (%)
0 297
67.8%
1 47
 
10.7%
2 27
 
6.2%
3 27
 
6.2%
4 23
 
5.3%
5 17
 
3.9%
ValueCountFrequency (%)
5 17
 
3.9%
4 23
 
5.3%
3 27
 
6.2%
2 27
 
6.2%
1 47
 
10.7%
0 297
67.8%

qualified_10
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
1
257 
0
181 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters438
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Length

2024-10-29T13:06:37.993977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:38.069444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Most occurring characters

ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 257
58.7%
0 181
41.3%

country_Andorra
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
437 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 437
99.8%
1.0 1
 
0.2%

Length

2024-10-29T13:06:38.151138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:38.224895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 437
99.8%
1.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 875
66.6%
. 438
33.3%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 875
66.6%
. 438
33.3%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 875
66.6%
. 438
33.3%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 875
66.6%
. 438
33.3%
1 1
 
0.1%

country_Armenia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-29T13:06:38.303108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:38.375925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Australia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
432 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 432
98.6%
1.0 6
 
1.4%

Length

2024-10-29T13:06:38.452699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:38.527435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 432
98.6%
1.0 6
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 870
66.2%
. 438
33.3%
1 6
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 870
66.2%
. 438
33.3%
1 6
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 870
66.2%
. 438
33.3%
1 6
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 870
66.2%
. 438
33.3%
1 6
 
0.5%

country_Austria
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-29T13:06:38.604269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:38.678611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Azerbaijan
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:38.755419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:38.849128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Belarus
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-29T13:06:38.949260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:39.043178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Belgium
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-29T13:06:39.143536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:39.238279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Bosnia and Herzegovina
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
433 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 433
98.9%
1.0 5
 
1.1%

Length

2024-10-29T13:06:39.338416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:39.427367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 433
98.9%
1.0 5
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 871
66.3%
. 438
33.3%
1 5
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 871
66.3%
. 438
33.3%
1 5
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 871
66.3%
. 438
33.3%
1 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 871
66.3%
. 438
33.3%
1 5
 
0.4%

country_Bulgaria
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-29T13:06:39.502690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:39.575434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Croatia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-29T13:06:39.672427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:39.766165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Cyprus
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:39.864434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:39.957311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Czech Republic
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
430 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 430
98.2%
1.0 8
 
1.8%

Length

2024-10-29T13:06:40.056716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:40.139518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 430
98.2%
1.0 8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 868
66.1%
. 438
33.3%
1 8
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 868
66.1%
. 438
33.3%
1 8
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 868
66.1%
. 438
33.3%
1 8
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 868
66.1%
. 438
33.3%
1 8
 
0.6%

country_Denmark
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:40.762940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:40.836453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Estonia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-29T13:06:40.915354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:40.988239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Finland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-29T13:06:41.065568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:41.140749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Georgia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-29T13:06:41.218922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:41.292534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Greece
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-29T13:06:41.368278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:41.443720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Hungary
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-29T13:06:41.520542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:41.596000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Iceland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-29T13:06:41.675316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:41.750832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Ireland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-29T13:06:41.853066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:41.948547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Israel
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-29T13:06:42.050877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:42.146475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Latvia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:42.227251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:42.315334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Lithuania
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-29T13:06:42.413471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:42.509283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Malta
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-29T13:06:42.584962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:42.661494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Moldova
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:42.741087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:42.819566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Montenegro
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-29T13:06:42.900448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:42.977118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Netherlands
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:43.059853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:43.134375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_North Macedonia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:43.216712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:43.293096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Norway
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:43.371152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:43.446980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Poland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-29T13:06:43.565626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:43.645885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Portugal
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
429 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 429
97.9%
1.0 9
 
2.1%

Length

2024-10-29T13:06:43.725341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:43.801430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 429
97.9%
1.0 9
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 867
66.0%
. 438
33.3%
1 9
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 867
66.0%
. 438
33.3%
1 9
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 867
66.0%
. 438
33.3%
1 9
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 867
66.0%
. 438
33.3%
1 9
 
0.7%

country_Romania
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:43.881130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:43.960940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Russia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-29T13:06:44.058273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:44.136201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_San Marino
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-29T13:06:44.215507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:44.288000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

country_Serbia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-29T13:06:44.363634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:44.440236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Slovakia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
435 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Length

2024-10-29T13:06:44.516806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:44.616905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

country_Slovenia
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
426 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Length

2024-10-29T13:06:44.719634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:44.793381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 426
97.3%
1.0 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 864
65.8%
. 438
33.3%
1 12
 
0.9%

country_Sweden
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
427 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Length

2024-10-29T13:06:44.871186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:44.945022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
97.5%
1.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 865
65.8%
. 438
33.3%
1 11
 
0.8%

country_Switzerland
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
425 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Length

2024-10-29T13:06:45.023920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:45.097222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 425
97.0%
1.0 13
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 863
65.7%
. 438
33.3%
1 13
 
1.0%

country_Turkey
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
435 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Length

2024-10-29T13:06:45.174973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:45.247873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

country_Ukraine
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
428 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Length

2024-10-29T13:06:45.344062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:45.429687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 428
97.7%
1.0 10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 866
65.9%
. 438
33.3%
1 10
 
0.8%

style_Dance
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
388 
1.0
50 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 388
88.6%
1.0 50
 
11.4%

Length

2024-10-29T13:06:45.506324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:45.582950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 388
88.6%
1.0 50
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 826
62.9%
. 438
33.3%
1 50
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 826
62.9%
. 438
33.3%
1 50
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 826
62.9%
. 438
33.3%
1 50
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 826
62.9%
. 438
33.3%
1 50
 
3.8%

style_Opera
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
435 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Length

2024-10-29T13:06:45.662355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:45.737289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 435
99.3%
1.0 3
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 873
66.4%
. 438
33.3%
1 3
 
0.2%

style_Pop
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
231 
1.0
207 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 231
52.7%
1.0 207
47.3%

Length

2024-10-29T13:06:45.813139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:45.886878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 231
52.7%
1.0 207
47.3%

Most occurring characters

ValueCountFrequency (%)
0 669
50.9%
. 438
33.3%
1 207
 
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 669
50.9%
. 438
33.3%
1 207
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 669
50.9%
. 438
33.3%
1 207
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 669
50.9%
. 438
33.3%
1 207
 
15.8%

style_Rock
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
407 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 407
92.9%
1.0 31
 
7.1%

Length

2024-10-29T13:06:45.967771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:46.040615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 407
92.9%
1.0 31
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 845
64.3%
. 438
33.3%
1 31
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 845
64.3%
. 438
33.3%
1 31
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 845
64.3%
. 438
33.3%
1 31
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 845
64.3%
. 438
33.3%
1 31
 
2.4%

style_Traditional
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
403 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 403
92.0%
1.0 35
 
8.0%

Length

2024-10-29T13:06:46.117537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:46.189814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 403
92.0%
1.0 35
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 841
64.0%
. 438
33.3%
1 35
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 841
64.0%
. 438
33.3%
1 35
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 841
64.0%
. 438
33.3%
1 35
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 841
64.0%
. 438
33.3%
1 35
 
2.7%

gender_Male
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
254 
1.0
184 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 254
58.0%
1.0 184
42.0%

Length

2024-10-29T13:06:46.266583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:46.343061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 254
58.0%
1.0 184
42.0%

Most occurring characters

ValueCountFrequency (%)
0 692
52.7%
. 438
33.3%
1 184
 
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 692
52.7%
. 438
33.3%
1 184
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 692
52.7%
. 438
33.3%
1 184
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 692
52.7%
. 438
33.3%
1 184
 
14.0%

gender_Mix
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0.0
400 
1.0
 
38

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 400
91.3%
1.0 38
 
8.7%

Length

2024-10-29T13:06:46.422787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T13:06:46.498799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 400
91.3%
1.0 38
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 838
63.8%
. 438
33.3%
1 38
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 838
63.8%
. 438
33.3%
1 38
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 838
63.8%
. 438
33.3%
1 38
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 838
63.8%
. 438
33.3%
1 38
 
2.9%

Interactions

2024-10-29T13:06:33.618097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.039256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.953268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.827957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.022104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.941119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.810631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.713699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.035193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.032629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.868843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.729502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.689829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.120601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.031536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.234477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.098136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.013226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.891496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.803741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.131839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.103201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.943160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.824275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.766788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.191078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.100903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.305042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.187741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.085500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.977781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.879127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.208127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.170688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.015998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.896621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.837625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.261492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.169161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.376818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.267118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.150863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.050232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:29.327549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.285063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.240046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.085835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.966482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.915696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.335739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.240549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.450668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.354295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.220527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.128948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:29.411662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.365264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.311972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.161321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.040688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:34.411448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.402813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.306675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.517440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.421407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.307436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.201023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:29.482442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.459679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.375767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.227059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.109440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:34.491122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.489835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.384307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.589607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.506354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.381046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.275306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:29.566022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.546719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.448951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.306021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.185699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:34.565215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.563033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.456346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.659391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.578767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.451020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.348078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:29.638718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.631930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.517648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.373644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.253044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:34.642567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.644325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.534494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.740135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.661237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.537060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.428088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:29.740224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.734936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.595707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.451547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.332123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:34.709996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.727551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.601960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.807643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.726590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.601921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.497138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:29.813437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.806038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.658657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.517502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.400706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:34.779331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.800626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.690393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.877612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.799920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.668459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.568846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:29.885311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.881558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.726456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.584830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.478342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:34.848949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:22.875312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:23.758397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:25.950152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:26.870467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:27.739112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:28.639672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:29.960044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:30.953379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:31.796252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:32.654501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T13:06:33.545384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-29T13:06:46.604518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BPMacousticnessbacking_dancersbacking_instrumentsbacking_singerscountry_Andorracountry_Armeniacountry_Australiacountry_Austriacountry_Azerbaijancountry_Belaruscountry_Belgiumcountry_Bosnia and Herzegovinacountry_Bulgariacountry_Croatiacountry_Cypruscountry_Czech Republiccountry_Denmarkcountry_Estoniacountry_Finlandcountry_Georgiacountry_Greececountry_Hungarycountry_Icelandcountry_Irelandcountry_Israelcountry_Latviacountry_Lithuaniacountry_Maltacountry_Moldovacountry_Montenegrocountry_Netherlandscountry_North Macedoniacountry_Norwaycountry_Polandcountry_Portugalcountry_Romaniacountry_Russiacountry_San Marinocountry_Serbiacountry_Slovakiacountry_Sloveniacountry_Swedencountry_Switzerlandcountry_Turkeycountry_Ukrainedanceabilityenergygender_Malegender_Mixhappinesskeylanguageloudness(dB)main_singersqualified_10semi_draw_positionstyle_Dancestyle_Operastyle_Popstyle_Rockstyle_Traditional
BPM1.000-0.2290.077-0.0390.0090.0000.0000.0000.0470.0490.0000.1020.1590.0380.0000.0000.1060.0000.1910.0000.0410.0940.0000.0000.0980.0000.0000.0000.0000.0000.2050.0760.0000.0000.0000.2160.0270.0000.0000.1390.0000.0000.0000.0000.0000.0760.1100.2630.0000.0000.1040.0300.060-0.1720.0150.0000.1210.2230.0000.1350.0650.130
acousticness-0.2291.000-0.173-0.062-0.0540.0000.0000.0000.0000.0000.0000.0630.1090.0000.1150.0000.0220.0000.0000.0000.0000.0000.0860.0000.0930.0610.0560.0000.0000.0000.0000.0000.0350.0000.0000.2020.0000.0970.0400.0750.1150.0000.0000.0700.0000.000-0.224-0.5210.0000.000-0.2650.0200.1780.213-0.0220.0780.0180.2000.1010.2410.0740.056
backing_dancers0.077-0.1731.000-0.314-0.1250.0000.0000.0000.0000.1490.0000.0000.0000.0000.0550.1170.0000.0000.0000.0000.0000.0230.0000.0000.0370.0880.0000.0000.1400.2780.0770.0540.0000.1010.1130.0000.0000.0000.0000.0970.0000.0000.0120.0000.1710.1750.2620.2340.0000.0760.196-0.0450.078-0.169-0.1470.056-0.0000.1400.0000.0000.1050.182
backing_instruments-0.039-0.062-0.3141.000-0.0570.1530.0000.0000.0000.0440.1000.0000.1780.1070.0000.0000.0840.1060.0000.1470.0510.0000.0640.0000.1580.0000.0600.0000.0640.0000.0000.0000.0000.0000.0100.0000.1070.0000.0000.0000.1810.0220.0280.1390.0280.000-0.0190.1240.1960.0000.073-0.0300.104-0.057-0.0220.117-0.0370.0000.0000.0660.5220.000
backing_singers0.009-0.054-0.125-0.0571.0000.1040.0000.0000.0880.0580.0000.0000.0000.0000.0780.0830.0000.0390.0000.0000.0000.0610.1010.1320.0960.1270.0580.0190.0380.0000.0000.0810.0000.0000.1530.0000.0000.0000.0840.0000.1040.0000.0000.0290.0000.0000.0100.1140.0470.0720.0590.1320.094-0.146-0.0580.114-0.0280.0000.0000.0000.0540.000
country_Andorra0.0000.0000.0000.1530.1041.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1190.0000.0000.1900.0000.0000.6940.0000.0000.0000.0000.0000.0000.0000.000
country_Armenia0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1140.0000.0000.0000.0000.0510.0000.0000.0000.0000.0730.0000.0000.0000.0000.000
country_Australia0.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0200.0000.0000.0000.0000.0000.0980.0000.0000.000
country_Austria0.0470.0000.0000.0000.0880.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1060.0910.0000.0000.1020.0000.0000.0550.0000.0000.0730.0000.0000.0000.0000.000
country_Azerbaijan0.0490.0000.1490.0440.0580.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0980.0000.0000.1180.0000.0760.0000.0000.0860.0790.0000.0000.0000.0000.000
country_Belarus0.0000.0000.0000.1000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.1290.0000.0000.0310.0000.0000.0000.000
country_Belgium0.1020.0630.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0930.0000.0000.0750.0230.0810.0000.0260.0000.0540.0000.0000.0420.0000.000
country_Bosnia and Herzegovina0.1590.1090.0000.1780.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.1000.2570.0210.0000.0380.0000.0710.0000.0000.0000.0000.000
country_Bulgaria0.0380.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1120.0000.0750.0000.0000.0320.0000.1800.0000.0830.0440.0000.0000.0000.000
country_Croatia0.0000.1150.0550.0000.0780.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.1350.0000.0420.0730.0250.0000.0580.0130.0000.000
country_Cyprus0.0000.0000.1170.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.1280.0000.0000.0560.0000.0620.0000.0000.0000.0000.000
country_Czech Republic0.1060.0220.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1500.0000.0000.0000.0760.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.000
country_Denmark0.0000.0000.0000.1060.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0480.0000.0000.0000.1370.000
country_Estonia0.1910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.000
country_Finland0.0000.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1270.000
country_Georgia0.0410.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1100.0000.0000.0000.0580.0000.0000.0000.0000.0000.0710.0000.0000.0000.0960.000
country_Greece0.0940.0000.0230.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1890.0620.0000.0000.0000.0000.0000.000
country_Hungary0.0000.0860.0000.0640.1010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1140.0900.0000.0000.0000.0320.0000.0000.0170.0000.0000.0000.0000.0000.000
country_Iceland0.0000.0000.0000.0000.1320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.1280.0000.0800.1210.0000.0420.0000.0000.0000.0000.000
country_Ireland0.0980.0930.0370.1580.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1250.0000.0000.0000.0000.0810.0000.0000.0330.1710.0000.0000.0000.0000.000
country_Israel0.0000.0610.0880.0000.1270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.0000.0000.0470.0000.000
country_Latvia0.0000.0560.0000.0600.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1180.0000.0000.0000.0710.0810.0760.0000.0000.1200.0500.0000.0000.0630.0000.000
country_Lithuania0.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.1260.1120.0000.1580.1120.0000.0000.0000.0000.0000.0000.000
country_Malta0.0000.0000.1400.0640.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.1090.1740.0810.0000.0000.0000.1180.0000.0000.0000.0000.000
country_Moldova0.0000.0000.2780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.1250.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.000
country_Montenegro0.2050.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0750.0930.0360.0000.0000.0000.0000.000
country_Netherlands0.0760.0000.0540.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.2150.0000.0710.0880.0000.0000.0000.0000.0000.0000.000
country_North Macedonia0.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.1200.1710.0000.0000.0000.0000.000
country_Norway0.0000.0000.1010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0330.0000.0000.0510.0000.0810.0000.0000.0000.000
country_Poland0.0000.0000.1130.0100.1530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1000.0000.0000.0000.0000.0000.0000.0630.0000.0330.0000.0000.0000.0000.000
country_Portugal0.2160.2020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.1670.0000.0000.0000.0000.1720.3950.2190.0000.0000.0000.0000.0300.0000.094
country_Romania0.0270.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0790.0810.0000.0000.0000.000
country_Russia0.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.0000.0000.0000.0000.0000.0000.0090.0660.0000.0000.0000.0000.0000.000
country_San Marino0.0000.0400.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.1260.0000.0000.0230.0000.0560.0000.0440.0000.0000.0000.000
country_Serbia0.1390.0750.0970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.1490.0000.0000.0000.0000.0000.1690.0000.1000.0000.0000.0000.0000.0000.0000.073
country_Slovakia0.0000.1150.0000.1810.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0000.0000.1120.0520.0000.0000.0000.0170.0000.000
country_Slovenia0.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0620.0540.0000.0000.0000.0460.0550.0000.0000.0000.0000.0000.0000.0000.000
country_Sweden0.0000.0000.0120.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0970.0000.0000.0000.1040.0770.0190.1070.0000.0770.0000.0000.0000.0000.0000.000
country_Switzerland0.0000.0700.0000.1390.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0330.0520.0000.0000.0000.0000.000
country_Turkey0.0000.0000.1710.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0030.0560.0000.0000.0000.1030.0000.0000.0000.0000.0000.0000.0000.0170.0000.000
country_Ukraine0.0760.0000.1750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0220.0000.0000.1760.0000.0000.0000.1020.0210.0000.0000.0000.0000.000
danceability0.110-0.2240.262-0.0190.0100.0000.1140.0000.1060.0000.0000.0000.1290.0000.0000.0000.1500.0000.0000.0000.1100.0000.0000.0000.0000.0000.1180.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.1020.0600.1490.0000.0000.0970.0000.0030.0001.0000.2970.0100.0000.585-0.0180.127-0.0970.0660.121-0.0280.1190.2120.2760.0000.138
energy0.263-0.5210.2340.1240.1140.1190.0000.0000.0910.0980.0000.0930.0000.1120.0000.0000.0000.0000.1600.0890.0000.0000.1140.0000.1250.0000.0000.1050.0000.0760.0000.0000.0000.0000.1000.1670.0000.0000.0000.0000.0000.0620.0000.0700.0560.0000.2971.0000.0000.0520.538-0.0350.000-0.6720.1570.0520.0050.2650.0000.2030.1380.035
gender_Male0.0000.0000.0000.1960.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0220.0100.0001.0000.2500.0960.0560.0000.0000.0630.0520.0000.0000.0000.0000.1830.000
gender_Mix0.0000.0000.0760.0000.0720.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.2501.0000.0660.0000.0000.0000.7010.0000.0000.0000.0000.0000.0500.000
happiness0.104-0.2650.1960.0730.0590.1900.0000.0170.1020.1180.0000.0750.1000.0000.0000.0890.0760.0000.0000.0000.0580.0000.0000.0550.0000.0000.0710.1260.1090.1250.0000.0330.0000.0500.0000.0000.0000.0000.1260.0000.0000.0000.1040.0000.0000.0000.5850.5380.0960.0661.0000.0370.065-0.2930.1600.088-0.0610.2150.1280.2120.1050.087
key0.0300.020-0.045-0.0300.1320.0000.0510.0000.0000.0000.0000.0230.2570.0000.0190.1280.0000.0340.0000.0000.0000.0000.0000.1280.0000.0000.0810.1120.1740.0000.0000.2150.0290.0000.0000.0000.0000.0000.0000.0000.0950.0000.0770.0000.1030.176-0.018-0.0350.0560.0000.0371.0000.089-0.0350.0100.045-0.0100.0650.0340.1080.0000.101
language0.0600.1780.0780.1040.0940.0000.0000.0200.0000.0760.0190.0810.0210.0320.1350.0000.0000.0000.0000.0000.0000.0000.0320.0000.0810.0000.0760.0000.0810.0000.0320.0000.0000.0330.0000.1720.0000.0000.0000.1690.0000.0460.0190.0000.0000.0000.1270.0000.0000.0000.0650.0891.0000.0000.1820.0500.1360.0550.0000.1630.0000.309
loudness(dB)-0.1720.213-0.169-0.057-0.1460.6940.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.1580.0000.0320.0000.0710.0000.0000.0000.3950.0000.0000.0230.0000.0000.0550.1070.0000.0000.000-0.097-0.6720.0000.000-0.293-0.0350.0001.000-0.0680.114-0.0550.0660.0000.0760.0210.141
main_singers0.015-0.022-0.147-0.022-0.0580.0000.0000.0000.0000.0000.1290.0260.0380.1800.0420.0560.0000.0000.0000.0000.0000.1890.0000.1210.0000.0000.0000.1120.0000.0000.0750.0880.0000.0000.0630.2190.0000.0090.0000.1000.1120.0000.0000.0000.0000.0000.0660.1570.0630.7010.1600.0100.182-0.0681.0000.0000.0260.0000.0000.0000.0000.111
qualified_100.0000.0780.0560.1170.1140.0000.0000.0000.0000.0860.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.0620.0170.0000.0330.0000.1200.0000.0000.0000.0930.0000.1200.0510.0000.0000.0000.0660.0560.0000.0520.0000.0770.0330.0000.1020.1210.0520.0520.0000.0880.0450.0500.1140.0001.0000.1950.0370.0000.0790.0000.017
semi_draw_position0.1210.018-0.000-0.037-0.0280.0000.0730.0000.0730.0790.0000.0540.0710.0830.0250.0620.0000.0480.0000.0000.0710.0000.0000.0420.1710.0830.0500.0000.1180.0000.0360.0000.1710.0000.0330.0000.0790.0000.0000.0000.0000.0000.0000.0520.0000.021-0.0280.0050.0000.000-0.061-0.0100.136-0.0550.0260.1951.0000.0590.0000.0000.0000.128
style_Dance0.2230.2000.1400.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0810.0000.0440.0000.0000.0000.0000.0000.0000.0000.1190.2650.0000.0000.2150.0650.0550.0660.0000.0370.0591.0000.0000.3300.0700.079
style_Opera0.0000.1010.0000.0000.0000.0000.0000.0980.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2120.0000.0000.0000.1280.0340.0000.0000.0000.0000.0000.0001.0000.0170.0000.000
style_Pop0.1350.2410.0000.0660.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0630.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0170.0000.0000.0000.0170.0000.2760.2030.0000.0000.2120.1080.1630.0760.0000.0790.0000.3300.0171.0000.2480.267
style_Rock0.0650.0740.1050.5220.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1370.0000.1270.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1380.1830.0500.1050.0000.0000.0210.0000.0000.0000.0700.0000.2481.0000.044
style_Traditional0.1300.0560.1820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.1380.0350.0000.0000.0870.1010.3090.1410.1110.0170.1280.0790.0000.2670.0441.000

Missing values

2024-10-29T13:06:35.021307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-29T13:06:35.289812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

semi_draw_positionlanguagemain_singerskeyBPMenergydanceabilityhappinessloudness(dB)acousticnessbacking_dancersbacking_singersbacking_instrumentsqualified_10country_Andorracountry_Armeniacountry_Australiacountry_Austriacountry_Azerbaijancountry_Belaruscountry_Belgiumcountry_Bosnia and Herzegovinacountry_Bulgariacountry_Croatiacountry_Cypruscountry_Czech Republiccountry_Denmarkcountry_Estoniacountry_Finlandcountry_Georgiacountry_Greececountry_Hungarycountry_Icelandcountry_Irelandcountry_Israelcountry_Latviacountry_Lithuaniacountry_Maltacountry_Moldovacountry_Montenegrocountry_Netherlandscountry_North Macedoniacountry_Norwaycountry_Polandcountry_Portugalcountry_Romaniacountry_Russiacountry_San Marinocountry_Serbiacountry_Slovakiacountry_Sloveniacountry_Swedencountry_Switzerlandcountry_Turkeycountry_Ukrainestyle_Dancestyle_Operastyle_Popstyle_Rockstyle_Traditionalgender_Malegender_Mix
0101174.611007076456550000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.0
1211246.941156975598100400.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.0
2301261.63935668508900010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3411349.23116184239118700010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.0
4501164.811206168606000400.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.01.00.0
5601293.6610582833251412210.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.01.00.0
6711293.66808452728300500.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.0
7801146.838748321563900010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
8901261.6314288709634100510.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.0
91006233.0883346438116800010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
semi_draw_positionlanguagemain_singerskeyBPMenergydanceabilityhappinessloudness(dB)acousticnessbacking_dancersbacking_singersbacking_instrumentsqualified_10country_Andorracountry_Armeniacountry_Australiacountry_Austriacountry_Azerbaijancountry_Belaruscountry_Belgiumcountry_Bosnia and Herzegovinacountry_Bulgariacountry_Croatiacountry_Cypruscountry_Czech Republiccountry_Denmarkcountry_Estoniacountry_Finlandcountry_Georgiacountry_Greececountry_Hungarycountry_Icelandcountry_Irelandcountry_Israelcountry_Latviacountry_Lithuaniacountry_Maltacountry_Moldovacountry_Montenegrocountry_Netherlandscountry_North Macedoniacountry_Norwaycountry_Polandcountry_Portugalcountry_Romaniacountry_Russiacountry_San Marinocountry_Serbiacountry_Slovakiacountry_Sloveniacountry_Swedencountry_Switzerlandcountry_Turkeycountry_Ukrainestyle_Dancestyle_Operastyle_Popstyle_Rockstyle_Traditionalgender_Malegender_Mix
4281001440.001329262402100400.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.0
4291111415.301259666814032000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.0
4301212311.1310895657121030010.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.0
4311311369.991289265655440010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.0
4321401220.0010358354086703110.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.0
4331501369.99908657525441010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.0
4341611207.651286768678032010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.0
4351711369.991259261854150010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.0
4361801174.611247269646302310.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
4371913415.301328966795403000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.0